Adversarial detection discriminates adversarial samples from the original data. Adversarial samples are generated by minimally perturbing actual samples to produce a change in the model's output, such as a misclassification. Most works rely on the knowledge of some attacks for training [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, with the exception of [8]}.
